7 papers
Quantum thermodynamics and semi-definite optimization
Nana Liu, Michele Minervini, Dhrumil Patel +1
In quantum thermodynamics, a system is described by a Hamiltonian and a list of non-commuting charges representing conserved quantities like particle number or electric charge, and…
Quantum natural gradient with thermal-state initialization
Michele Minervini, Dhrumil Patel, Mark M. Wilde
Parameterized quantum circuits (PQCs) are central to variational quantum algorithms (VQAs), yet their performance is hindered by complex loss landscapes that make their trainabilit…
Digital Quantum Simulations of the Non-Resonant Open Tavis-Cummings Model
Aidan N. Sims, Dhrumil Patel, Aby Philip +4
The open Tavis--Cummings model consists of quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum i…
Evolved Quantum Boltzmann Machines
Michele Minervini, Dhrumil Patel, Mark M. Wilde
We introduce evolved quantum Boltzmann machines as a variational ansatz for quantum optimization and learning tasks. Given two parameterized Hamiltonians and , an evol…
Sample-based Hamiltonian and Lindbladian simulation: Non-asymptotic analysis of sample complexity
Byeongseon Go, Hyukjoon Kwon, Siheon Park +2
Density matrix exponentiation (DME) is a quantum algorithm that processes multiple copies of a program state to realize the Hamiltonian evolution . Wave matrix Lindb…
Natural gradient and parameter estimation for quantum Boltzmann machines
Dhrumil Patel, Mark M. Wilde
Thermal states play a fundamental role in various areas of physics, and they are becoming increasingly important in quantum information science, with applications related to semi-d…